Electrostatic potential as a reactivity scoring function in computer-assisted enzyme engineering

The high catalytic efficiency of enzymes is attained, in part, by their capacity to stabilize electrostatically the transition state of the chemical reaction. High-throughput protocols for measuring this electrostatic contribution in computer-assisted enzyme design are limited. We present here an easy-to-compute metric that captures the electrostatic complementarity of the enzyme to the charge distribution of the substrate at the transition state. We demonstrate such a complementarity for a representative dataset of glycoside hydrolases, a large family of enzymes responsible for the hydrolytic cleavage of glycosidic bonds in oligosaccharides, polysaccharides, and glycoconjugates. We have implemented this metric in BindScan, a computer-based mutational analysis protocol to assist protein engineering. We demonstrate the predictive power of BindScan with this metric for two mechanistically distinct glycoside hydrolases: Spodoptera frugiperda β-glucosidase (Sfβgly, operates via protein nucleophile catalysis) and Bifidobacterium bifidum lacto-N-biosidase (BbLnbB, operates via substrate-assisted catalysis). The metric correctly predicts sequence positions sensible to the modulation of k_cat/K_M upon mutation from an experimental benchmark of 51 mutants of Sfβgly with 77% classification efficiency and identifies variants of BbLnbB with improved transglycosylation yields (up to 32%). Based on electrostatic potential and ligand affinity calculations, as implemented in BindScan, we propose a rational strategy to design glycoside hydrolase variants with improved transglycosylation efficiency for the synthesis of added-value glycoconjugates. The new reactivity metric may contribute to expanding the range of computational protocols available to assist enzyme engineering campaigns aimed at optimizing mechanistically relevant properties.